Triple
T13272630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mutiara SIS Al-Jufrie Airport |
E316102
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
WAML
WAML is the ICAO airport code for Mutiara SIS Al-Jufrie Airport in Palu, Central Sulawesi, Indonesia.
|
E1030191
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: WAML | Statement: [Mutiara SIS Al-Jufrie Airport, ICAOcode, WAML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WAML Context triple: [Mutiara SIS Al-Jufrie Airport, ICAOcode, WAML]
-
A.
WAMO
WAMO is a Pittsburgh-area radio station historically known for its urban contemporary and hip-hop programming serving the region’s Black community.
-
B.
WAMM
WAMM is the ICAO airport code for Sam Ratulangi International Airport serving Manado in North Sulawesi, Indonesia.
-
C.
WAMM
WAMM is the abbreviation for the World Association of the Major Metropolises, an international organization that brings together and represents the interests of the world’s largest cities.
-
D.
WAM
WAM is a university art museum in Johannesburg, South Africa, known for its extensive collection of African art and its role in research and education at the University of the Witwatersrand.
-
E.
WLM
WLM (Workload Manager) is an IBM z/OS component that dynamically manages and prioritizes system workloads to meet performance goals and service-level objectives.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: WAML Triple: [Mutiara SIS Al-Jufrie Airport, ICAOcode, WAML]
Generated description
WAML is the ICAO airport code for Mutiara SIS Al-Jufrie Airport in Palu, Central Sulawesi, Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WAML Target entity description: WAML is the ICAO airport code for Mutiara SIS Al-Jufrie Airport in Palu, Central Sulawesi, Indonesia.
-
A.
WAMO
WAMO is a Pittsburgh-area radio station historically known for its urban contemporary and hip-hop programming serving the region’s Black community.
-
B.
WAMM
WAMM is the ICAO airport code for Sam Ratulangi International Airport serving Manado in North Sulawesi, Indonesia.
-
C.
WAMM
WAMM is the abbreviation for the World Association of the Major Metropolises, an international organization that brings together and represents the interests of the world’s largest cities.
-
D.
WAM
WAM is a university art museum in Johannesburg, South Africa, known for its extensive collection of African art and its role in research and education at the University of the Witwatersrand.
-
E.
WLM
WLM (Workload Manager) is an IBM z/OS component that dynamically manages and prioritizes system workloads to meet performance goals and service-level objectives.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99020f710819094c2618662bdc7fd |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a51d458819080b8c8f3a4df0f52 |
completed | May 3, 2026, 8:41 a.m. |
| NEDg | Description generation | batch_69f70b117c588190bb81ff53664cac4a |
completed | May 3, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f70c04da34819091e01db25741674e |
completed | May 3, 2026, 8:49 a.m. |
Created at: April 9, 2026, 9:26 p.m.