Triple
T15932990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arayat |
E386367
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Candaba |
E386369
|
NE FINISHED |
How this triple was built (2 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: Candaba | Statement: [Arayat, locatedNear, Candaba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Candaba Context triple: [Arayat, locatedNear, Candaba]
-
A.
Candaba
chosen
Candaba is a municipality in the Philippine province of Pampanga known for its vast wetlands and rich bird-watching sites.
-
B.
Kasaba
Kasaba is a 1997 Turkish drama film by acclaimed director Nuri Bilge Ceylan, noted for its quiet, contemplative portrayal of rural family life and childhood.
-
C.
Kadmat
Kadmat is a coral island in India’s Lakshadweep archipelago, known for its white-sand beaches, clear lagoons, and vibrant marine life that make it a popular destination for snorkeling and diving.
-
D.
Dombay
Dombay is a mountain resort settlement in the North Caucasus of Russia, known for its alpine scenery, skiing, and hiking opportunities.
-
E.
Kandan
Kandan is a locality within Beijing’s Fengtai District, known primarily as a residential and urban neighborhood area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156a6d9b88190b461d12d69b12ac0 |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb5b514108190965e77346d8b476e |
completed | May 9, 2026, 10:31 p.m. |
Created at: April 10, 2026, 4:53 a.m.