Triple

T11001858
Position Surface form Disambiguated ID Type / Status
Subject IRS-1B E260020 entity
Predicate primaryInstrument P933 FINISHED
Object LISS-I
LISS-I is a multispectral imaging sensor used on early Indian Remote Sensing (IRS) satellites to capture medium-resolution Earth observation data for applications like agriculture and land-use mapping.
E898966 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: LISS-I | Statement: [IRS-1B, primaryInstrument, LISS-I]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LISS-I
Context triple: [IRS-1B, primaryInstrument, LISS-I]
  • A. LSS
    LSS is the station code for LaSalle Street Station, a major commuter rail terminal in downtown Chicago, Illinois.
  • B. LI
    LI is the Roman numeral representing the number 51.
  • C. LI
    LI is the two-letter ISO 3166-1 alpha-2 country code for Liechtenstein.
  • D. LSL
    LSL is the currency code for the Lesotho loti, the official monetary unit of Lesotho.
  • E. LIS
    LIS is the three-letter IATA airport code for Humberto Delgado Airport, the main international airport serving Lisbon, Portugal.
  • 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: LISS-I
Triple: [IRS-1B, primaryInstrument, LISS-I]
Generated description
LISS-I is a multispectral imaging sensor used on early Indian Remote Sensing (IRS) satellites to capture medium-resolution Earth observation data for applications like agriculture and land-use mapping.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: LISS-I
Target entity description: LISS-I is a multispectral imaging sensor used on early Indian Remote Sensing (IRS) satellites to capture medium-resolution Earth observation data for applications like agriculture and land-use mapping.
  • A. LSS
    LSS is the station code for LaSalle Street Station, a major commuter rail terminal in downtown Chicago, Illinois.
  • B. LI
    LI is the Roman numeral representing the number 51.
  • C. LI
    LI is the two-letter ISO 3166-1 alpha-2 country code for Liechtenstein.
  • D. LSL
    LSL is the currency code for the Lesotho loti, the official monetary unit of Lesotho.
  • E. LIS
    LIS is the three-letter IATA airport code for Humberto Delgado Airport, the main international airport serving Lisbon, Portugal.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d796d760008190930228fa77b61b8b completed April 9, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
NEDg Description generation batch_69e35570b0bc8190a939b0c8e3ce8105 completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e359508a388190a16d48a17015e13e completed April 18, 2026, 10:13 a.m.
Created at: April 8, 2026, 9:25 p.m.