Triple
T22837620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Bet |
E565988
|
entity |
| Predicate | settingLocation |
P40
|
FINISHED |
| Object | Manila |
—
|
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: Manila | Statement: [Big Bet, settingLocation, Manila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manila Context triple: [Big Bet, settingLocation, Manila]
-
A.
Manila
Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
-
B.
Manila
Manila is a web-based content management and blogging system developed by UserLand Software that was popular in the early days of personal publishing on the internet.
-
C.
Manila
Manila is a fictional member of the Professor's heist crew in the Spanish television series "Money Heist" ("La Casa de Papel").
-
D.
Manila
chosen
Manila is the capital city of the Philippines, a historic and densely populated coastal metropolis that has long served as the country’s political, economic, and cultural center.
-
E.
Quezon City, Philippines
Quezon City, Philippines is a highly urbanized city in Metro Manila that serves as a major political, educational, and commercial center of the country.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e303cec81909c5c118dc8c93354 |
completed | April 29, 2026, 3:42 a.m. |
Created at: April 17, 2026, 3:35 p.m.