Triple
T4108011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nueva Ecija |
E88501
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Laur
Laur is a rural municipality in the province of Nueva Ecija in the Philippines, known for its agricultural landscape and proximity to the Sierra Madre mountain range.
|
E413960
|
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: Laur | Statement: [Nueva Ecija, hasMunicipality, Laur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laur Context triple: [Nueva Ecija, hasMunicipality, Laur]
-
A.
Lauri
Lauri is a Finnish given name commonly used for men, notably borne by former President of Finland Lauri Kristian Relander.
-
B.
Laurie
Laurie is a charming, wealthy, and impulsive young man who becomes a close friend and would-be suitor to the March sisters in Louisa May Alcott’s novel "Little Women."
-
C.
Lori
Lori is a feminine given name commonly used in English-speaking countries, often as a diminutive of Laura or Lorraine.
-
D.
Lana
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
-
E.
Laura
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
- 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: Laur Triple: [Nueva Ecija, hasMunicipality, Laur]
Generated description
Laur is a rural municipality in the province of Nueva Ecija in the Philippines, known for its agricultural landscape and proximity to the Sierra Madre mountain range.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laur Target entity description: Laur is a rural municipality in the province of Nueva Ecija in the Philippines, known for its agricultural landscape and proximity to the Sierra Madre mountain range.
-
A.
Lauri
Lauri is a Finnish given name commonly used for men, notably borne by former President of Finland Lauri Kristian Relander.
-
B.
Laurie
Laurie is a charming, wealthy, and impulsive young man who becomes a close friend and would-be suitor to the March sisters in Louisa May Alcott’s novel "Little Women."
-
C.
Lori
Lori is a feminine given name commonly used in English-speaking countries, often as a diminutive of Laura or Lorraine.
-
D.
Lana
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
-
E.
Laura
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af019e23c481909578eba1c9270282 |
completed | March 9, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b8240248190afd026a450958d4c |
completed | March 14, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69b56cbf12348190836f79e509468a3d |
completed | March 14, 2026, 2:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b570aef9008190bf8ef2deb00178ae |
completed | March 14, 2026, 2:29 p.m. |
Created at: March 9, 2026, 3:40 p.m.