Triple
T14608599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLAR platform |
E342897
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
CLAR
CLAR is a digital platform designed to streamline and manage research-related administrative and compliance processes.
|
E1108380
|
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: CLAR | Statement: [CLAR platform, abbreviation, CLAR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CLAR Context triple: [CLAR platform, abbreviation, CLAR]
-
A.
CLARI
CLARI is the UN/LOCODE identifier for Chacalluta International Airport, the main airport serving Arica in northern Chile.
-
B.
CLD
CLD is the IATA airport code for McClellan–Palomar Airport serving the Carlsbad area in Southern California.
-
C.
LEAR
LEAR (Low Energy Antiproton Ring) was a CERN facility designed to store and decelerate antiprotons for precision experiments in antimatter physics.
-
D.
CLRAE
CLRAE is the acronym for the Congress of Local and Regional Authorities, a Council of Europe institution representing local and regional governments across member states.
-
E.
CLARITY
CLARITY is a tissue-clearing technique that renders biological tissues transparent while preserving their molecular and structural integrity for high-resolution imaging and analysis.
- 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: CLAR Triple: [CLAR platform, abbreviation, CLAR]
Generated description
CLAR is a digital platform designed to streamline and manage research-related administrative and compliance processes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CLAR Target entity description: CLAR is a digital platform designed to streamline and manage research-related administrative and compliance processes.
-
A.
CLARI
CLARI is the UN/LOCODE identifier for Chacalluta International Airport, the main airport serving Arica in northern Chile.
-
B.
CLD
CLD is the IATA airport code for McClellan–Palomar Airport serving the Carlsbad area in Southern California.
-
C.
LEAR
LEAR (Low Energy Antiproton Ring) was a CERN facility designed to store and decelerate antiprotons for precision experiments in antimatter physics.
-
D.
CLRAE
CLRAE is the acronym for the Congress of Local and Regional Authorities, a Council of Europe institution representing local and regional governments across member states.
-
E.
CLARITY
CLARITY is a tissue-clearing technique that renders biological tissues transparent while preserving their molecular and structural integrity for high-resolution imaging and analysis.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44d327c8190a8d20568429d0f80 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94d09e988190a2a2a1332397b412 |
completed | May 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69fd9828129c8190bd7445e99dadc618 |
completed | May 8, 2026, 8 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd98cf0bcc81909dac826a32daaf04 |
completed | May 8, 2026, 8:03 a.m. |
Created at: April 10, 2026, 1:25 a.m.