Triple
T1666419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Manila |
E36022
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Pasay
Pasay is a highly urbanized coastal city in the Philippines known for its entertainment complexes, shopping centers, and proximity to Manila’s main international airport.
|
E188579
|
NE FINISHED |
How this triple was built (4 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: Pasay | Statement: [Metro Manila, containsCity, Pasay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pasay Context triple: [Metro Manila, containsCity, Pasay]
-
A.
Pasig
Pasig is a highly urbanized city in Metro Manila in the Philippines, known historically as a riverside settlement and now as a major commercial and residential center.
-
B.
Vina
Vina is an alternate given name of Fay Wray, the Canadian-American actress best known for her iconic role in the 1933 film "King Kong."
-
C.
Ponte Verde
Ponte Verde is a bridge in Trieste, Italy, that spans the city’s Canal Grande in its historic center.
-
D.
Miraflores
Miraflores is an upscale coastal district of Lima, Peru, known for its shopping, dining, nightlife, and cliffside views over the Pacific Ocean.
-
E.
Puerto Ayacucho
Puerto Ayacucho is a Venezuelan city that serves as the capital of Amazonas state and a key gateway to the Amazon rainforest region.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Pasay Triple: [Metro Manila, containsCity, Pasay]
Generated description
Pasay is a highly urbanized coastal city in the Philippines known for its entertainment complexes, shopping centers, and proximity to Manila’s main international airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pasay Target entity description: Pasay is a highly urbanized coastal city in the Philippines known for its entertainment complexes, shopping centers, and proximity to Manila’s main international airport.
-
A.
Malpaso
Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
-
B.
Pasig
Pasig is a highly urbanized city in Metro Manila in the Philippines, known historically as a riverside settlement and now as a major commercial and residential center.
-
C.
Vina
Vina is an alternate given name of Fay Wray, the Canadian-American actress best known for her iconic role in the 1933 film "King Kong."
-
D.
Ponte Verde
Ponte Verde is a bridge in Trieste, Italy, that spans the city’s Canal Grande in its historic center.
-
E.
Miraflores
Miraflores is an upscale coastal district of Lima, Peru, known for its shopping, dining, nightlife, and cliffside views over the Pacific Ocean.
- F. None of above. chosen
Provenance (5 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90adc57cc8190b270004c363768e3 |
completed | March 5, 2026, 4:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad682fd42881908ecd0f331e81aba8 |
completed | March 8, 2026, 12:14 p.m. |
| NEDg | Description generation | batch_69ad69c8af5481909d73d90e1dcaf560 |
completed | March 8, 2026, 12:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad6a4aebb88190a27350216add031b |
completed | March 8, 2026, 12:23 p.m. |
Created at: March 4, 2026, 7:29 p.m.