Triple
T20025651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tacloban Port |
E494974
|
entity |
| Predicate | connectsTo |
P845
|
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: [Tacloban Port, connectsTo, Manila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manila Context triple: [Tacloban Port, connectsTo, 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.
San Miguel, Manila
San Miguel, Manila is a historic district in the city of Manila, Philippines, known for housing the presidential Malacañang Palace and various government and educational institutions.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6628d5b8c8190a35f95ac4a016550 |
completed | April 20, 2026, 5:29 p.m. |
Created at: April 11, 2026, 3:35 p.m.