Triple
T12167163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palo Seco |
E289862
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | San Juan |
E252451
|
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: San Juan | Statement: [Palo Seco, nearbyCity, San Juan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Juan Context triple: [Palo Seco, nearbyCity, San Juan]
-
A.
San Juan
San Juan is an Argentine wine-producing region recognized for its significant Malbec production.
-
B.
San Juan
chosen
San Juan is a suburban town in Trinidad and Tobago located just east of the capital, Port of Spain, known for its bustling commercial activity and residential communities.
-
C.
San Juan
San Juan is a highly urbanized city in Metro Manila, Philippines, known for its historical sites, dense residential and commercial areas, and role in the capital region’s urban core.
-
D.
San Juan
San Juan is a coastal municipality on Siquijor Island in the Philippines known for its beaches, dive spots, and laid-back tourist resorts.
-
E.
San Juan
San Juan is a coastal municipality in the Philippine province of Batangas known for its beaches, dive spots, and heritage sites.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915d85c088190a74fb7590877659b |
completed | April 10, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66509208190b7206e78df41c2fe |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:50 p.m.