Triple

T17794651
Position Surface form Disambiguated ID Type / Status
Subject Ermita E444257 entity
Predicate hasStationCode P1289 FINISHED
Object ER
ER is the station code for Ermita station, a stop on Manila’s Light Rail Transit system in the Philippines.
 E1288379 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: ER | Statement: [Ermita, hasStationCode, ER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ER
Context triple: [Ermita, hasStationCode, ER]
  • A. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • B. ER
    ER is the abbreviation used to designate the Eastern Region of British Rail, a major administrative division of the former British railway network covering eastern England.
  • C. ER
    ER is the standard abbreviation used for the Erie Otters, a junior ice hockey team in the Ontario Hockey League.
  • D. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • E. ER
    ER is the ISO 3166-1 alpha-2 country code for Eritrea, a nation in the Horn of Africa.
  • 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: ER
Triple: [Ermita, hasStationCode, ER]
Generated description
ER is the station code for Ermita station, a stop on Manila’s Light Rail Transit system in the Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ER
Target entity description: ER is the station code for Ermita station, a stop on Manila’s Light Rail Transit system in the Philippines.
  • A. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • B. ER
    ER is the abbreviation used to designate the Eastern Region of British Rail, a major administrative division of the former British railway network covering eastern England.
  • C. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • D. ER
    ER is the ISO 3166-1 alpha-2 country code for Eritrea, a nation in the Horn of Africa.
  • E. ER
    ER is the two-letter ISO 3166-1 alpha-2 country code for Eritrea, a nation in the Horn of Africa.
  • 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_69d8b9efe370819095cd219b143ae727 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48799f9608190bc97264c849278a0 completed April 19, 2026, 7:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02f83c9c388190ba48c3644114c4bc completed May 12, 2026, 9:51 a.m.
NEDg Description generation batch_6a02f9bcaf748190872b29f1427db09a completed May 12, 2026, 9:58 a.m.
NED2 Entity disambiguation (via description) batch_6a02fa679d1c8190bd61fc29a95ac565 completed May 12, 2026, 10:01 a.m.
Created at: April 10, 2026, 10:13 a.m.