Triple
T8079219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batangas |
E188572
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Lipa |
E682196
|
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: Lipa | Statement: [Batangas, hasCity, Lipa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lipa Context triple: [Batangas, hasCity, Lipa]
-
A.
Lipa
Lipa is a highly urbanized city in the province of Batangas in the Calabarzon region of the Philippines, known as a commercial, educational, and religious center.
-
B.
Rutooro
Rutooro is a Bantu language spoken primarily by the Tooro people in western Uganda.
-
C.
Mwinilunga
Mwinilunga is a town in northwestern Zambia known as an administrative and commercial center near the borders with Angola and the Democratic Republic of the Congo.
-
D.
Mbalizi
Mbalizi is a town in southwestern Tanzania located within the Mbeya Region, known as a local commercial and transport hub for the surrounding rural areas.
-
E.
Lipa City
chosen
Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
- 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_69ca82b50c708190863f661d438e68df |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb40a3f01c819096a2c9d5d5199fe6 |
completed | March 31, 2026, 3:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63f79ac08190af49e77bee67921d |
completed | April 1, 2026, 12:16 a.m. |
Created at: March 30, 2026, 5:28 p.m.