Triple
T21292489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mesa Reservoir |
E524830
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Makati |
—
|
NE NERFINISHED |
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: Makati | Statement: [La Mesa Reservoir, cityServed, Makati]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makati Context triple: [La Mesa Reservoir, cityServed, Makati]
-
A.
Makati
chosen
Makati is a highly urbanized city in Metro Manila, Philippines, known as the country’s leading financial and business center.
-
B.
Quezon City
Quezon City is a major urban center in Metro Manila known for hosting many national government institutions, universities, and media networks in the Philippines.
-
C.
Metro Manila
Metro Manila is the densely populated national capital region of the Philippines, encompassing Manila and several surrounding cities as the country’s political, economic, and cultural center.
-
D.
Manila
Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
-
E.
Manila
Manila is a fictional member of the Professor's heist crew in the Spanish television series "Money Heist" ("La Casa de Papel").
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73855e5d08190aed5e285247b4e23 |
completed | April 21, 2026, 8:41 a.m. |
Created at: April 16, 2026, 4:04 p.m.